Results 91 to 100 of about 1,461,923 (295)
Machine Learning and Knowledge ExtractionSpectral clustering has established itself as a powerful technique for data partitioning across various domains due to its ability to handle complex cluster structures.
Abderrafik Laakel Hemdanou +5 more
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Exploring the Potential of Spectral Classification in Estimation of Soil Contaminant Elements
Remote Sensing, 2017 Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security.
Weichao Sun +3 more
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PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining.
Y. Z. Gu +5 more
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FEBS Letters, EarlyView.Vacuolar H+‐ATPase (V‐ATPase) is a multisubunit rotary proton pump that acidifies intracellular compartments. Using elastic network models, we identified conserved hinge residues that govern its large‐scale functional motions. We show that intrinsically encoded ‘soft modes’ drive critical functional transitions, including rotary catalysis and ...
Diksha Parwana +3 more
wiley +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSpectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist.
Chengmao Wu, Jiale Zhang
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Approximate sparse spectral clustering based on local information maintenance for hyperspectral image classification. [PDF]
PLoS ONE, 2018 Sparse spectral clustering (SSC) has become one of the most popular clustering approaches in recent years. However, its high computational complexity prevents its application to large-scale datasets such as hyperspectral images (HSIs).
Qing Yan +4 more
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Outlier Cluster Formation in Spectral Clustering
CoRR, 2017 10 pages, 2 figures, 2 ...
Takuro Ina +5 more
openaire +2 more sources
Molecular Oncology, EarlyView.In the present work, we have identified a transcriptional signature based on the differential expression of six genes (BCL2&MAST4, HSH2D&LAT2, METRN&PITPNM2) that would facilitate the early detection of T‐cell acute lymphoblastic leukemia (T‐ALL) patients prone to a poor treatment response and could be implemented at diagnosis, along with other risk ...
Antonio Lahera +11 more
wiley +1 more source
Molecular Oncology, EarlyView.Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary +1 more
wiley +1 more source
Spectral clustering for TRUS images
BioMedical Engineering OnLine, 2007 Background Identifying the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy. Prostate volume is also important for prostate cancer diagnosis.
Salama Magdy MA, Mohamed Samar S
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